|
699
|
Matching Pursuit With Time-Frequency Dictionaries
– St'ephane Mallat, Zhifeng Zhang
- 1993
|
|
416
|
Entropy-Based Algorithms For Best Basis Selection
– Ronald R. Coifman, Mladen Victor Wickerhauser
- 1992
|
|
6234
|
Maximum likelihood from incomplete data via the EM algorithm
– A. P. Dempster, N. M. Laird, D. B. Rubin
- 1977
|
|
15
|
Comparison of Basis Selection Methods
– J. Adler, B. D. Rao, K. Kreutz-delgado
- 1996
|
|
8
|
Analysis and extensions of the FOCUSS algorithm
– B D Rao
- 1996
|
|
49
|
Neuromagnetic source imaging with focuss: a recursive weighted minimum norm algorithm. Electroenceph. clin
– I F Gorodnitsky, J S George, B D Rao
- 1995
|
|
200
|
Non-negative matrix factorization with sparseness constraints
– Patrik O. Hoyer, Peter Dayan
- 2004
|
|
132
|
Sparse signal reconstruction from limited data using FOCUSS: A re-weighted minimum norm algorithm
– Irina F. Gorodnitsky, Bhaskar D. Rao
- 1997
|
|
1089
|
Atomic decomposition by basis pursuit
– Scott Shaobing Chen, David L. Donoho, Michael, A. Saunders
- 1998
|
|
7
|
Frame based signal representation and compression. Unpublished doctoral dissertation, Stavanger Universty
– K Engan
- 2000
|
|
14
|
An improved FOCUSS-based learning algorithm for solving sparse linear inverse problems
– J Murray, K Kreutz-Delgado
- 2001
|
|
16
|
On minimum entropy segmentation
– David L. Donoho
- 1994
|
|
8
|
Measures and algorithms for best basis selection
– K Kreutz-Delgado, B Rao
- 1998
|
|
18
|
Signal processing with sparseness constraints
– B D Rao, Y Bresler
- 1998
|
|
84
|
A Probabilistic Framework for the Adaptation and Comparison of Image Codes
– Michael S. Lewicki, Bruno A. Olshausen
- 1999
|
|
149
|
Blind Source Separation by Sparse Decomposition in a Signal Dictionary
– M. Zibulevsky, B. A. Pearlmutter, P. Bofill, P. Kisilev
- 2000
|
|
683
|
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
– B A Olshausen, D J Field
- 1996
|
|
2333
|
Convex Analysis
– r t rockafellar
- 1970
|
|
41
|
A variational method for learning sparse and overcomplete representations. Neural computation
– M Girolami
|